Visual pattern recognition using neural networks

Neural networks have been widely studied in a number of fields, such as neural architectures, neurobiology, statistics of neural network and pattern classification. In the field of pattern classification, neural network models are applied on numerous applications, for instance, character recognition, speech recognition, and object recognition. Among these, character recognition is commonly used to illustrate the feature and classification characteristics of neural networks. In this dissertation, the theoretical foundations of artificial neural networks are first reviewed and existing neural models are studied. The Adaptive Resonance Theory (ART) model is improved to achieve more reasonable classification results. Experiments in applying the improved model to image enhancement and printed character recognition are discussed and analyzed. We also study the theoretical foundation of Neocognitron in terms of feature extraction, convergence in training, and shift invariance. We investigate the use of multilayered perceptrons with recurrent connections as the general purpose modules for image operations in parallel architectures. The networks are trained to carry out classification rules in image transformation. The training patterns can be derived from user-defmed transformations or from loading the pair of a sample image and its target image when the prior knowledge of transformations is unknown. Applications of our model include image smoothing, enhancement, edge detection, noise removal, morphological operations, image filtering, etc. With a number of stages stacked up together we are able to apply a series of operations on the image. That is, by providing various sets of training patterns the system can adapt itself to the concatenated transformation. We also discuss and experiment in applying existing neural models, such as multilayered perceptron, to realize morphological operations and other commonly used imaging operations. Some new neural architectures and training algorithms for the implementation of morphological operations are designed and analyzed. The algorithms are proven correct and efficient. The proposed morphological neural architectures are applied to construct the feature extraction module of a personal handwritten character recognition system. The system was trained and tested with scanned image of handwritten characters. The feasibility and efficiency are discussed along with the experimental results

Quantification of Order in Point Patterns

Pattern attributes are important in many disciplines, e.g. developmental biology, but there are few objective measures of them. Here we concentrate on the attribute of order in point patterns and its objective measurement. We examine perception of order and develop analysis algorithms that quantify the attribute in accordance with perception of it. Based on pairwise ranking of point patterns by degree of order, we show that judgements are highly consistent across individuals and that the perceptual dimension has an interval scale structure, spanning roughly 10 just-noticeable differences (jnds) between disorder and order. We designed a geometric algorithm that estimates order to an accuracy of half a jnd by quantifying the variability of the spaces between points. By anchoring the output of the algorithm so that Poisson point processes score on average 0, and perfect lattices score 10, we constructed an absolute interval scale of order. We demonstrated its utility in biology by quantifying the order of the Drosophila dorsal thorax epithelium during development. The psychophysical scaling method used relies on the comparison of stimuli with similar levels of order yielding a discrimination-based scale. As with other perceptual dimensions, an interesting question is whether supra-threshold perceptual differences are consistent with this scale. To test that we collected discrimination data, and data based on comparison of perceptual differences. Although the judgements of perceptual differences were found to be consistent with an interval scale, like the discrimination judgements, no common interval scale that could predict both sets of data was possible. Point patterns are commonly displayed as arrangements of dots. To examine how presentation parameters (dot size, dot numbers, and pattern area) affect discrimination, we collected discrimination data for ten presentation conditions. We found that discrimination performance depends on the ratio ‘dot diameter / average dot spacing’